Transparent access to multiple bioinformatics information sources (TAMBIS)
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Transcript of Transparent access to multiple bioinformatics information sources (TAMBIS)
Transparent access to multiple bioinformatics information sources (TAMBIS)
Goble, C.A. et al. (2001)IBM Systems Journal40(2), 532-551
Genome AnalysisPaper Presentation
March 24, 2005
Presentation Overview Why the need to integrate Definitions (“MW”s) Biologists’ burden What is TAMBIS The TaO Brains of TAMBIS What makes TAMBIS “service-oriented”? GRAIL TAMBIS Architecture What can you do at TAMBIS? Related Work More current Work Ongoing challenges for integration
Why the need to Integrate? The Molecular Biology Database Collection has 500+
resources 719 in 2005 NAR DB issue Adding ~150 in the past two years
Independent development and differing scopes heterogeneous formats, interfaces, input, outputs
Most popular resources : DNA and Protein sequences (GenBank, Swiss-Prot) Genome data (ACeDB) Protein structure and motifs (PDB, PROSITE) Similarity searching (BLAST)
Definitions (MW*) Extensional coverage :
number of entries / instances covered by the source
Intensional coverage :number of information fields /meta-data in each source
Description Logic :A family of knowledge representation languages which can be used to represent the terminological knowledge of an application domain in a structured and formally well-understood way.
CPL (Collection Programming Language) :A functional multidatabase language; models complex data types such as lists, sets, and variants with drivers (wrappers) that execute requests over data sources
* MW = “misunderstood word” (from a Montessori class)
Definitions (MW*) Terminology server :
Encapsulates the reasoning services associated with the Description Logic, supporting concept reasoning, role sanctioning, thesaurus, extrinsics services
Sanctioning :Capability of inferring more (biological) concepts by way of compositional constraints encompassed in the ontology
Ontology :An explicit formal specification of how to represent the objects, concepts, and other entities that are assumed to exist in some area of interest and the relationships that hold among them.
* MW = “misunderstood word” (from a Montessori class)
Biologists’ burden Construct a view of the meta-data Resolve structural and semantic differences in
the information Locate and communicate with the sources Interoperate between resources Transformation process
…. “fragile” process…. undoubtably specialized
TAMBIS A prototype mediation system
Designed to lessen the burden as described previously Service-oriented Based on an extensive source-independent global
ontology of molecular biology and bioinformatics Represented in a Description Logic Managed by a terminology server
A mixed top-down and bottom-up iterative methodology
Providing a single access point for biological information sources around the world
Emphasis of TAMBIS High transparency Read-only access Retrieval-oriented architecture
Efficiency and correctness Heterogeneity management Visual query interface
Features of TAMBIS Very rich domain ontology (1,800 biological
concepts) Web-based…
Query formation Ontology browsing
Query translation and planning process
More than GO, more than SRS
The TaO Aim is to capture biological and bioinformatics
knowledge in a logical conceptual framework Constraints… or features…
Only biologically sensible concepts classify correctly
Can encompass different user views Makes biological concepts and their
relationships computationally accessible
Could have used another ontology but this one was developed concurrently for TAMBIS
The TaO
Current state of TaO Big Model
Covers proteins, nucleic acids, their components, function, location, publishing
Baby model (Baby TaO) Covers only the protein subset of the big model Used for the “fully functional version” of
TAMBIS Reconciled model
Merged version of the big and baby TAMBIS ontologies
Brains of TAMBIS … Query translation and planning process
“A concept formed as a query is resolved when its extension is retrieved”Sample query,
Protein which hasFunction Receptor
Takes a query phrased in terms of the conceptual layer and converts it into an executable plan in terms of the classes and methods of the physical layer.
Plans an efficient way of executing a queryi.e., evaluates the alternatives paths
The various resources do not need to provide query language interfaces
(Definitions revisited)
concept
relationship
What makes TAMBIS “service-oriented”? Reasoning services for description logics
Subsumption Classification Satisfiability Retrieval
Sanctioned term construction Querying Terminology Services
(Definitions revisited)
sanctionsubsumption
An example of subsumption
GRAIL A concept modelling language A Description Logic in the KL-ONE family….
In this case, used to describe biological concepts
Two major services provided : Supporting transitive roles, role hierarchies, a
powerful set of concept assertion axioms Novel multilayered sanctioning mechanism
TAMBIS Architecture Three layers (“models”)
Physical Conceptual Mapping
Five components Ontology of biological
terms (A) Knowledge-driven query
formulation interface (B) Sources and Services
Model linking the biological ontology with the source schemas (C)
Query transformation rewriting process (D)
Wrapper service dealing with external sources (E)
Query translation
What can you do at TAMBIS? Browse the ontology Build a query with a visual interface and
reference to an ontology Give values to concepts (for a query) Identify desired concepts as results Bookmark your queries
Ontology browser
Specific questions for TAMBIS Find human homologues of yeast receptor
proteins Find rat proteins that have a domain with a
seven-propeller domain architecture Find the binding sites of human enzymes with
zinc cofactors
…. How many sources are involved per question?…. How difficult to find these answers without integration?.... For someone unfamiliar with the resources?
TAMBIS OverviewNatural language :Select motifs for antigenic human proteins that participate in apoptosis and are homologous to the lymphocyte associated receptor of death (also known as lard).
TAMBIS Translation :Select patterns in the proteins that invoke an immunological response and participate in programmed cell death that are similar in their sequence of amino acids to the protein that is associate with triggering cell death in the white cells of the immune system.
Concept expression in GRAIL :Motif which<isComponentOf (Protein which
<hasOrganismClassification Species FunctionsInProcess Apoptosis HasFunction Antigen isHomologousTo Protein which
<hasName ProteinName>)>)>(Species given value “human” and ProteinName given value “lard”)
Related Work Closest work : Object-Protocol Model (OPM)
No source transparency SRS, Entrez, BioNavigator
Does not handle as complex queries TAMBIS is query based, these are clicking-
based BioKleisli, DiscoveryLink
Middleware solutions, TAMBIS sits on top of this Carnot
General rather than detailed ontology
More current work DAML + OIL (new DL for TAMBIS)
DARPA Agent Markup Language – provides a rich set of constructs to create ontologies and to markup information so that it is machine-readable
CPL/BioKleisli (wrapper language) replaced by DiscoveryHub (commercial)
GO – more completely and widely used Protégé OWL
Ontology editor for the Semantic Web BioMOBY, BioConductor
Complementary systems
Ongoing challenges to integration Evaluation
Technical efficiency User usability
Changing underlying resources Resources disappear Changes in popularity MAINTENANCE
…. Widespread acceptance and use?
References Goble, C.A. et al. (2001) “Transparent access to
multiple bioinformatics information resources.” IBM Systems Journal. 40(2), 532-551.
Baker, P.G. et al. (1999) “An ontology for bioinformatics applications.” Bioinformatics. 15(6), 510-520.
Ontology definition : dli.grainger.uiuc.edu/glossary.htm Description Logic defn :
www.absoluteastronomy.com/encyclopedia/D/De/Description_Logic.htm
TAMBIS website :http://imgproj.cs.man.ac.uk/tambis/